# last away ~ lives
merged_data %>%
select(lives, last_away, subject_id) %>%
distinct() %>%
ggplot(., aes(x = factor(lives), y = last_away, fill = factor(lives))) +
geom_boxplot(notch = T) +
theme(panel.background = element_rect(fill = "white"),
legend.position = "none") +
labs(x = "Lives", y = "Distance to Ghost at Turnaround", fill = "Lives", title = "Turn Around Distance By Number of Remaining Lives", subtitle = "Subjects stayed farther away from the ghost when they only had one life remaining")
# last away ~ rewardgroup
merged_data %>%
select(rewardgroup, last_away, subject_id) %>%
distinct() %>%
ggplot(., aes(x = factor(rewardgroup), y = last_away, fill = factor(rewardgroup))) +
geom_boxplot(notch = T) +
theme(panel.background = element_rect(fill = "white"),
legend.position = "none") +
labs(x = "rewardgroup", y = "Distance to Ghost at Turnaround", fill = "Rewardgroup", title = "Turn Around Distance By rewardgroups", subtitle = "Subjects moved closer to the ghost when there was a large reward \nin the last position (w/o ghost overlap)")
Notes:
- filter picks 16-20 subjects
- subject 49,33,36 never 1 life?
to-do:
- interpretations
random sample, individual + combined plots
- lives –> last away
- rewardgroup –> last away
- score –> last away
lives_ind
rewardgroup_ind
score_ind
#rm(lives_ind, rewardgroup_ind, score_ind)
plot_comb <- lives_comb/rewardgroup_comb/score_comb
plot_comb
#rm(lives_comb, rewardgroup_comb, score_comb, plot_comb)
with random subsample for rewardgroup, lives and score
note:
- difference between plots in No RE ≠ 0
–> due to different calculations / package inconsistency
(plot_model)?
–> verify with other functions (e.g. dotplot(ranef(model, condVar =
TRUE)))
to-do:
- header e.g. which model
- interpretations
- verification differences RE outputs
print("Reward Model")
## [1] "Reward Model"
reward_RE <- plot_model(model.reward.random.noanxiety,type="re", grid.breaks = 10)
## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.5.4.1
## Current Matrix version is 1.4.1
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.3
## Current TMB version is 1.9.6
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)
reward_RE # + theme_sjplot() #white background etc
## Warning: Removed 1 rows containing missing values (`geom_point()`).
reward_RE_sorted <- plot_model(model.reward.random.noanxiety,type="re", sort.est = T, grid=F)
reward_RE_sorted
## [[1]]
##
## [[2]]
print("Loss Model")
## [1] "Loss Model"
loss_RE <- plot_model(model.loss.random.noanxiety,type="re")
loss_RE
loss_RE_sorted <- plot_model(model.loss.random.noanxiety,type="re", sort.est = T, grid=F)
loss_RE_sorted
## [[1]]
##
## [[2]]
print("Extensive Model")
## [1] "Extensive Model"
extensive_RE <- plot_model(model.extensive.random1.noanxiety,type="re", grid.breaks = 5, grid=T)
extensive_RE
extensive_RE_sorted <- plot_model(model.extensive.random1.noanxiety,type="re", sort.est = T, grid=F)
extensive_RE_sorted
## [[1]]
##
## [[2]]
reward_verification <- dotplot(ranef(model.extensive.random1.noanxiety, condVar = TRUE))
reward_verification
## $subject_id
method: plot_model(model,type=“diag”)
print("Reward Model")
## [1] "Reward Model"
reward_diagnostic <- plot_model(model.reward.random.noanxiety,type="diag",
terms=c("rewardgroup","subject_id"))
reward_diagnostic
## [[1]]
##
## [[2]]
## [[2]]$subject_id
##
##
## [[3]]
##
## [[4]]
print("Loss Model")
## [1] "Loss Model"
loss_diagnostic <- plot_model(model.loss.random.noanxiety,type="diag",
terms=c("lives","subject_id"))
loss_diagnostic
## [[1]]
##
## [[2]]
## [[2]]$subject_id
##
##
## [[3]]
##
## [[4]]
print("Extensive Model")
## [1] "Extensive Model"
extensive_diagnostic <- plot_model(model.extensive.random1.noanxiety,type="diag",
terms=c("rewardgroup","lives", "subject_id"))
extensive_diagnostic
## [[1]]
##
## [[2]]
## [[2]]$subject_id
##
##
## [[3]]
##
## [[4]]